In this paper, we present a new modification of Viola-Jones complex classifiers. We describe a complex classifier in the form of a decision tree and provide a method of training for such classifiers. Performance impact of the tree structure is analyzed. Comparison is carried out of precision and performance of the presented method with that of the classical cascade. Various tree architectures are experimentally studied. The task of vehicle wheels detection on images obtained from an automatic vehicle classification system is taken as an example.